Spectral analysis on time-course expression data

Detecting periodic genes using a real-valued iterative adaptive approach

Kwadwo S. Agyepong, Fang Han Hsu, Edward R. Dougherty, Erchin Serpedin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities, which are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the proposed methods suffer from restrictions and large false positives to a certain extent. Additionally, in some experiments, arbitrarily irregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task. A novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive approach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then analyzed using Fisher's hypothesis test. With a proper p-value threshold, periodic genes can be detected. A periodic signal, two nonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition, two yeast real datasets were applied for validation. The simulations and real data analysis reveal that RIAA can perform competitively with the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and when the number of cycles covered by the sampling time points is very reduced.

Original languageEnglish
Article number171530
JournalAdvances in Bioinformatics
Volume2013
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Spectrum analysis
Genes
Sampling
Periodicity
Yeast
Signal processing
Circadian Rhythm
Cells
Sample Size
Noise
Spectrum Analysis
Cell Cycle
Yeasts
Experiments

ASJC Scopus subject areas

  • Computer Science Applications
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Biomedical Engineering

Cite this

Spectral analysis on time-course expression data : Detecting periodic genes using a real-valued iterative adaptive approach. / Agyepong, Kwadwo S.; Hsu, Fang Han; Dougherty, Edward R.; Serpedin, Erchin.

In: Advances in Bioinformatics, Vol. 2013, 171530, 2013.

Research output: Contribution to journalArticle

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